Graph Representation Learning.
Early graph representation learning (GRL) methods learn node embeddings by utilizing probability models on the generated random walk paths on graphs
[1]}, [2]}. However, these methods overly emphasize the structural information while ignore the important attribute information.
Thanks to the development of graph neural networks (GNNs), GNN-based GRL methods that jointly exploit graph structure information and node attribute information in spectral or spatial domain have been widely studied in recent years. These methods can be roughly categorized into auto-encoder-based methods [3]}, [4]}, [5]}, [6]}, [7]} and contrastive learning-based methods [8]}, [9]}, [10]}, [11]}, [12]}.
One underlying assumption commonly adopted by current GRL methods is that all node attributes are complete. However, in practice, the absent data makes it difficult to utilize the existing methods for satisfactory performance.
